CHC analysis
Dan Bruzzese
6/28/2020
using chc 6
present CV LDAs, have testing/training if need be
graphed LDAs are all the points.
MANOVA for species level
fit_full_species_man$pca_summary ## Importance of first k=7 (out of 35) components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.0413 0.7144 0.51001 0.38099 0.34918 0.30663 0.28705
## Proportion of Variance 0.3871 0.1822 0.09287 0.05183 0.04354 0.03357 0.02942
## Cumulative Proportion 0.3871 0.5694 0.66225 0.71408 0.75762 0.79119 0.82061summary(manova(as.matrix(data[,4:cols]) ~ hostRace * sex *site, data = data), test = "Wilks")## Df Wilks approx F num Df den Df Pr(>F)
## hostRace 4 0.04507 53.399 28 1097.5 < 2.2e-16 ***
## sex 1 0.71370 17.421 7 304.0 < 2.2e-16 ***
## site 8 0.26950 8.085 56 1642.4 < 2.2e-16 ***
## hostRace:sex 4 0.80887 2.375 28 1097.5 8.147e-05 ***
## hostRace:site 1 0.96866 1.405 7 304.0 0.202640
## sex:site 7 0.77071 1.663 49 1547.8 0.002979 **
## hostRace:sex:site 1 0.97168 1.266 7 304.0 0.266990
## Residuals 310
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PCA for Species Level
LDA for Species level
## Reference
## Prediction Cingulata Cornivora Mendax pom Zepheria
## Cingulata 29 0 0 0 0
## Cornivora 1 5 0 1 0
## Mendax 0 0 60 8 2
## pom 0 0 1 214 0
## Zepheria 0 0 0 1 15## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.584570e-01 9.213168e-01 9.312796e-01 9.771049e-01 6.646884e-01
## AccuracyPValue McnemarPValue
## 2.679678e-40 NaN
Manova for pomonella
summary(manova(as.matrix(data[,4:cols]) ~ hostRace * site * sex, data = data), test = "Wilks")## Df Wilks approx F num Df den Df Pr(>F)
## hostRace 1 0.70245 12.2841 7 203.00 4.504e-13 ***
## site 4 0.37641 8.1524 28 733.35 < 2.2e-16 ***
## sex 1 0.48494 30.8012 7 203.00 < 2.2e-16 ***
## hostRace:site 2 0.72182 5.1338 14 406.00 6.340e-09 ***
## hostRace:sex 1 0.95257 1.4440 7 203.00 0.189418
## site:sex 4 0.77940 1.8745 28 733.35 0.004284 **
## hostRace:site:sex 1 0.92648 2.3014 7 203.00 0.028089 *
## Residuals 209
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1LDA for Pomonella
## Reference
## Prediction Apple Haw
## Apple 80 22
## Haw 26 96## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.857143e-01 5.693688e-01 7.261263e-01 8.375728e-01 5.267857e-01
## AccuracyPValue McnemarPValue
## 8.355647e-16 6.650055e-01
LDA predicting host and sex for pomonella
## Reference
## Prediction Apple_Female Apple_Male Haw_Female Haw_Male
## Apple_Female 51 5 17 2
## Apple_Male 1 21 1 8
## Haw_Female 19 1 44 5
## Haw_Male 0 8 4 37## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.830357e-01 5.662230e-01 6.177391e-01 7.434095e-01 3.169643e-01
## AccuracyPValue McnemarPValue
## 3.310270e-29 5.581411e-01
MANOVA for Zepheria
# no interaction because missing males at MtPleasant
summary(manova(as.matrix(data[,4:cols]) ~ sex + site, data = data), test = "Wilks")## Df Wilks approx F num Df den Df Pr(>F)
## sex 1 0.74708 0.67708 5 10 0.6507
## site 1 0.83749 0.38808 5 10 0.8461
## Residuals 14LDA for Zepheria
## Reference
## Prediction Zepheria_Female Zepheria_Male
## Zepheria_Female 5 3
## Zepheria_Male 4 5## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5882353 0.1793103 0.3292472 0.8155630 0.5294118
## AccuracyPValue McnemarPValue
## 0.4062810 1.0000000
LDA for sex +site zepheria
## Reference
## Prediction Zepheria_Female_EastLansing
## Zepheria_Female_EastLansing 2
## Zepheria_Female_MtPleasant 0
## Zepheria_Male_EastLansing 4
## Reference
## Prediction Zepheria_Female_MtPleasant
## Zepheria_Female_EastLansing 2
## Zepheria_Female_MtPleasant 1
## Zepheria_Male_EastLansing 0
## Reference
## Prediction Zepheria_Male_EastLansing
## Zepheria_Female_EastLansing 1
## Zepheria_Female_MtPleasant 5
## Zepheria_Male_EastLansing 2## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.29411765 -0.05699482 0.10313551 0.55958272 0.47058824
## AccuracyPValue McnemarPValue
## 0.95792652 0.03207164
MANOVA for Mendax
summary(manova(as.matrix(data[,4:cols]) ~ sex * site, data = data), test = "Wilks")## Df Wilks approx F num Df den Df Pr(>F)
## sex 1 0.57727 6.1024 6 50 7.541e-05 ***
## site 2 0.10716 17.1232 12 100 < 2.2e-16 ***
## sex:site 2 0.74705 1.3082 12 100 0.2258
## Residuals 55
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1LDA for Mendax
## Reference
## Prediction Mendax_Female Mendax_Male
## Mendax_Female 20 15
## Mendax_Male 13 13## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.54098361 0.07072905 0.40849889 0.66935590 0.54098361
## AccuracyPValue McnemarPValue
## 0.55241652 0.85010674
LDA for sex +site mendax
## Reference
## Prediction Mendax_Female_Fenville Mendax_Female_OtisLake
## Mendax_Female_Fenville 7 1
## Mendax_Female_OtisLake 1 5
## Mendax_Female_Sewanee 0 2
## Mendax_Male_Fenville 6 0
## Mendax_Male_OtisLake 0 1
## Mendax_Male_Sewanee 0 0
## Reference
## Prediction Mendax_Female_Sewanee Mendax_Male_Fenville
## Mendax_Female_Fenville 0 5
## Mendax_Female_OtisLake 1 0
## Mendax_Female_Sewanee 4 1
## Mendax_Male_Fenville 0 5
## Mendax_Male_OtisLake 1 1
## Mendax_Male_Sewanee 4 0
## Reference
## Prediction Mendax_Male_OtisLake Mendax_Male_Sewanee
## Mendax_Female_Fenville 0 0
## Mendax_Female_OtisLake 1 2
## Mendax_Female_Sewanee 2 3
## Mendax_Male_Fenville 0 1
## Mendax_Male_OtisLake 1 1
## Mendax_Male_Sewanee 2 3## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.409836066 0.283523654 0.285504382 0.543223627 0.229508197
## AccuracyPValue McnemarPValue
## 0.001280557 NaN
MANOVA for cingulata
summary(manova(as.matrix(data[,4:cols]) ~ sex * site, data = data), test = "Wilks")## Df Wilks approx F num Df den Df Pr(>F)
## sex 1 0.80383 1.2813 4 21 0.308955
## site 2 0.39036 3.1529 8 42 0.006927 **
## sex:site 2 0.66131 1.2059 8 42 0.319040
## Residuals 24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1LDA for Cingulata
## Reference
## Prediction Cingulata_Female Cingulata_Male
## Cingulata_Female 8 6
## Cingulata_Male 7 9## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5666667 0.1333333 0.3742735 0.7453925 0.5000000
## AccuracyPValue McnemarPValue
## 0.2923324 1.0000000
LDA for sex +site cingulata
## Reference
## Prediction Cingulata_Female_Fenville
## Cingulata_Female_Fenville 1
## Cingulata_Female_SouthBend 0
## Cingulata_Female_Urbana 0
## Cingulata_Male_Fenville 1
## Cingulata_Male_SouthBend 0
## Cingulata_Male_Urbana 1
## Reference
## Prediction Cingulata_Female_SouthBend Cingulata_Female_Urbana
## Cingulata_Female_Fenville 3 0
## Cingulata_Female_SouthBend 2 0
## Cingulata_Female_Urbana 4 1
## Cingulata_Male_Fenville 0 0
## Cingulata_Male_SouthBend 0 0
## Cingulata_Male_Urbana 1 1
## Reference
## Prediction Cingulata_Male_Fenville Cingulata_Male_SouthBend
## Cingulata_Female_Fenville 0 4
## Cingulata_Female_SouthBend 0 1
## Cingulata_Female_Urbana 0 1
## Cingulata_Male_Fenville 0 1
## Cingulata_Male_SouthBend 0 1
## Cingulata_Male_Urbana 0 2
## Reference
## Prediction Cingulata_Male_Urbana
## Cingulata_Female_Fenville 0
## Cingulata_Female_SouthBend 2
## Cingulata_Female_Urbana 0
## Cingulata_Male_Fenville 2
## Cingulata_Male_SouthBend 0
## Cingulata_Male_Urbana 0## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.17241379 0.04000000 0.05845608 0.35774755 0.34482759
## AccuracyPValue McnemarPValue
## 0.98826571 NaN
MANOVA for Cornivora
There is only one site
# no site because only sampled at one site.
summary(manova(as.matrix(data[,4:cols]) ~ sex, data = data), test = "Wilks")## Df Wilks approx F num Df den Df Pr(>F)
## sex 1 0.54944 0.82004 2 2 0.5494
## Residuals 3LDA for cornivora
## Reference
## Prediction Cornivora_Female Cornivora_Male
## Cornivora_Female 0 2
## Cornivora_Male 0 2## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.50000000 0.00000000 0.06758599 0.93241401 1.00000000
## AccuracyPValue McnemarPValue
## 1.00000000 0.47950012
lines of diversification
euclidian distance
## Apple_Female Apple_Male Cingulata_Female Cingulata_Male
## Apple_Female 0.0000000
## Apple_Male 1.1052241 0.0000000
## Cingulata_Female 1.4738114 1.6036342 0.0000000
## Cingulata_Male 1.4051571 1.6128575 0.1130547 0.0000000
## Cornivora_Female 1.8164505 1.6514454 0.4825512 0.5956054
## Cornivora_Male 2.3568964 1.7473686 1.3312107 1.4407462
## Haw_Female 0.5292825 1.5234184 1.9508336 1.8698619
## Haw_Male 0.6482415 0.5290546 1.6459586 1.6184269
## Mendax_Female 1.1742028 2.0634150 1.2374012 1.1243501
## Mendax_Male 1.0269548 1.8389388 1.0200228 0.9078379
## Zepheria_Female 2.2810030 3.3164335 2.4854319 2.3757562
## Zepheria_Male 2.3942415 3.4468577 2.6586999 2.5484713
## Cornivora_Female Cornivora_Male Haw_Female Haw_Male
## Apple_Female
## Apple_Male
## Cingulata_Female
## Cingulata_Male
## Cornivora_Female 0.0000000
## Cornivora_Male 0.8731903 0.0000000
## Haw_Female 2.3257992 2.8860880 0.0000000
## Haw_Male 1.8371458 2.1364704 1.0022950 0.0000000
## Mendax_Female 1.7199319 2.5456134 1.3364646 1.7570533
## Mendax_Male 1.5002006 2.3102051 1.2814072 1.5661386
## Zepheria_Female 2.9567427 3.8164525 2.1620829 2.9257376
## Zepheria_Male 3.1317505 3.9891585 2.2408291 3.0416391
## Mendax_Female Mendax_Male Zepheria_Female Zepheria_Male
## Apple_Female
## Apple_Male
## Cingulata_Female
## Cingulata_Male
## Cornivora_Female
## Cornivora_Male
## Haw_Female
## Haw_Male
## Mendax_Female 0.0000000
## Mendax_Male 0.2497539 0.0000000
## Zepheria_Female 1.3207469 1.5704754 0.0000000
## Zepheria_Male 1.4794064 1.7289277 0.1809107 0.0000000mds plot
centroids
site centroids
